Prediction of compressive strength of desert sand concrete after high temperature based on an improved BP model

Liu Haifeng; Liu Haotian; Li Luoying; Chen Xiaolong; Che Jialing; Yang Weiwu

doi:10.16186/j.cnki.1673-9787.2024030006

Abstract：Objectives To investigate the effect of high-temperature history on the compressive strength of desert sand concrete(DSC),compressive strength tests of DSC after exposure to high temperatures were conducted by considering the effects of desert sand replacement rate(DSRR),temperature,heating rate,and resting time.Methods X-ray diffraction(XRD)and scanning electron microscopy(SEM)were em-ployed to analyze the changes in the microstructure and phase composition of DSC after high-temperature exposure.Based on the back-propagation(BP)algorithm,an artificial neural network(ANN)for predict-ing the compressive strength of DSC after high temperature was developed by integrating particle swarm op-timization(PSO)and genetic algorithm(GA).The model was validated using ten-fold cross-validation.Re-sults The results indicated that the compressive strength of DSC decreased with increasing temperature,ac-companied by significant decomposition of hydration products and progressive propagation and interconnec-tion of microcracks.Longer resting time led to higher compressive strength,while higher heating rates ac-celerated the damage of DSC.The compressive strength reached its maximum when the DSRR was 20%.The mean absolute percentage errors(MAPE)of the three predictive models were all within 8%.A higher degree of model optimization resulted in smaller prediction errors.The hybrid PSO-GA neural network model achieved the highest prediction accuracy,with a root mean square error(RMSE)of 1.127 2,MAPE of 3.98%,and a determination coefficient(R2)of 0.987 8 for the 28-day compressive strength.Conclu-sions The proposed method significantly improves the prediction accuracy of the mechanical properties of DSC after exposure to high temperature.